Quantitative Researcher
At a glance
Mid-level Data Science role at Point72. London · full-time · no salary stated · we estimate £62,250–£94,375.
Point72 has not published a salary for this role. The figure below is our estimate, not theirs.
dataroles summary, based on the employer's posting.
What you'll do
- Search equity markets for repeatable patterns that could support mid-frequency trading strategies
- Build research ideas into live strategies, covering data preparation, backtests, tuning and production rollout
- Assess new datasets for their potential to predict stock returns
- Keep live portfolio trading running and improve how it operates
- Help build research tools that support work at scale
What you bring
- A master's or doctorate in a quantitative subject
- Up to two years of professional experience
- Strong Python skills and experience working with large datasets
- Fluency in data science methods such as feature creation
Who this fits
This role suits an early-career researcher who is curious, motivated and able to investigate ideas independently while collaborating with a team. You'll focus on systematic equity strategies and need a genuine interest in financial markets; prior market experience is not essential. The role is based in London.
From the employer
About Cubist
Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.
Role:
A new Cubist portfolio management team specializing in the systematic trading of equities is looking for a Quant Researcher whose core focus will be working on mid-frequency alpha strategies. Joining the team will provide a unique opportunity to be involved with the early stages of a product launch and develop within a growing team.
Responsibilities:
- Perform rigorous and innovative research to discover systematic anomalies in the equities market
- End-to-end development, including alpha idea generation, data processing, strategy backtesting, optimization, and production implementation
- Identify and evaluate new datasets for stock return prediction
- Maintain and improve portfolio trading in a production environment
- Contribute to the analysis framework for scalable research
Requirements:
- MS or PhD in a quantitative discipline
- 0-2 years of professional work experience
- A background in financial markets is not necessary, but an interest in the field is essential
- Proven expertise in Python and handling large datasets
- Fluency in data science practices, e.g., feature engineering. Experience with machine learning is a plus
- Highly motivated, curious, and critical thinker
- Collaborative mindset with strong independent research abilities
- Commitment to the highest ethical standards
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